KnowNet: building a large net of knowledge from the web
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/12503
Tipus de documentText en actes de congrés
Data publicació2008
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
This paper presents a new fully automatic
method for building highly dense and accurate knowledge bases from existing
semantic resources. Basically, the method uses a wide-coverage and accurate knowledge-based Word Sense Disambiguation
algorithm to assign the most appropriate senses to large sets of topically related words acquired from the web.
KnowNet, the resulting knowledge-base
which connects large sets of semantically related concepts is a major step towards
the autonomous acquisition of knowledge
from raw corpora. In fact, KnowNet is several
times larger than any available knowledge
resource encoding relations between
synsets, and the knowledge KnowNet contains
outperform any other resource when is empirically evaluated in a common framework.
CitacióCuadros, M.; Rigau, G. KnowNet: building a large net of knowledge from the web. A: International Conference on Computational Linguistics. "22nd International Conference on Computational Linguistics". Manchester: 2008, p. 1-8.
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cuadrosrigau08a.pdf | article | 120,9Kb | Visualitza/Obre |